EDBT 2026 Demo / reviewers in the wild / expert
Subramanian Ramamoorthy
dblp:97/5598
· DBLP profile ↗
67ranked-venue papers
3as first author
24since 2021 · last 2026
0000-0002-6300-5103ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 60 · 2 first-author · 21 since 2021Systems, architecture and hardware · 32 · 1 first-author · 15 since 2021Graphics, computer vision, multimedia, augmented reality and games · 5 · 1 first-author · 2 since 2021Applied, interdisciplinary, general and emerging computing · 4 · 3 since 2021Databases, data management, data science and information retrieval · 3 · 1 since 2021Human-computer interaction and ubiquitous computing · 2Computer networks · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | MSPW: Monitoring Simulated Physical Weakness Using Detailed Behavioral Features and Personalized ModelingabstractAging and chronic conditions affect older adults’ daily lives, making the early detection of developing health issues crucial. Weakness, which is common across many conditions, can subtly alter physical movements and daily activities. However, these behavioral changes can be difficult to detect because they are gradual and often masked by natural day-to-day variability. To isolate the behavioral phenotype of weakness while controlling for confounding factors, this study simulates physical weakness in healthy adults through exercise-induced fatigue, providing interpretable insights into potential behavioral indicators for long-term monitoring. A non-intrusive camera sensor is used to monitor individuals’ daily sitting and relaxing activities over multiple days, allowing us to observe behavioral changes before and after simulated weakness. The system captures fine-grained features related to body motion, inactivity, and environmental context in real time while prioritizing privacy. A Bayesian Network models the relationships among activities, contextual factors, and behavioral indicators. Fine-grained features, including non-dominant upper-body motion speed and scale, together with inactivity distribution, are most effective when used with a 300-second window. Personalized models achieve 0.97 accuracy at distinguishing simulated weak days from normal days, and no universal set of optimal features or activities is observed across participants. Muhammad Ahmed Raza, Craig Innes, Subramanian Ramamoorthy, Robert B. Fisher |
ACM Trans. Comput. Heal. | 4 |
| 2025 | Learning Visually Grounded Domain Ontologies via Embodied Conversation and ExplanationabstractIn this paper, we offer a learning framework in which the agent's knowledge gaps are overcome through corrective feedback from a teacher whenever the agent explains its (incorrect) predictions. We test it in a low-resource visual processing scenario, in which the agent must learn to recognize distinct types of toy truck. The agent starts the learning process with no ontology about what types of truck exist nor which parts they have, and a deficient model for recognizing those parts from visual input. The teacher's feedback to the agent's explanations addresses its lack of relevant knowledge in the ontology via a generic rule (e.g., "dump trucks have dumpers"), whereas an inaccurate part recognition is corrected by a deictic statement (e.g., "this is not a dumper"). The learner utilizes this feedback not only to improve its estimate of the hypothesis space of possible domain ontologies and probability distributions over them but also to use those estimates to update its visual interpretation of the scene. Our experiments demonstrate that teacher-learner pairs utilizing explanations and corrections are more data-efficient than those without such a faculty. Jonghyuk Park 0004, Alex Lascarides, Subramanian Ramamoorthy |
AAAI | 3 |
| 2025 | Beyond Discriminant Patterns: On the Robustness of Decision Rule EnsemblesabstractLocal decision rules are highly regarded for their interpretability, offering insights into granular patterns that are critical for explainable machine learning. While existing methods emphasize the identification of discriminative patterns to achieve high predictive accuracy, they often fail to account for robustness against distributional shifts that occur during deployment. This paper addresses this gap by proposing a novel approach to learning and ensembling local decision rules that are inherently robust across diverse training and deployment environments. Our method leverages causal inference principles, viewing distributional shifts as interventions on the underlying system. We incorporate two regularization techniques: graph-based regularization, which decomposes invariant features using causal graphs, and variance-based regularization, which promotes stability by introducing artificial features to guide decision boundaries. These techniques enable the generation of decision rules that excel in predictive power while maintaining stability under changing environmental conditions. Extensive experiments on synthetic and benchmark datasets validate the effectiveness of the proposed method. The results demonstrate significant improvements in robustness, outperforming traditional boosting ensembles when subjected to diverse and challenging environments. Quantitative and qualitative analyses further highlight how the integration of causal knowledge and adaptive regularization encourages the utilization of invariant features, leading to better generalization. This work emphasizes the importance of causal reasoning in the design of machine learning models, paving the way for future research into robust, interpretable, and reliable decision-making frameworks for real-world applications. Xin Du 0006, Subramanian Ramamoorthy, Wouter Duivesteijn, Mykola Pechenizkiy |
ICDM | 2 |
| 2024 | Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment CollaborationabstractLarge, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin |
ICRA | 221 |
| 2024 | Generating robotic elliptical excisions with human-like tool-tissue interactionsabstractIn surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces. Arturas Straizys, Michael Burke, Subramanian Ramamoorthy |
ICRA | 3 |
| 2024 | Adaptive Splitting of Reusable Temporal Monitors for Rare Traffic ViolationsabstractAutonomous Vehicles (AVs) are often tested in simulation to estimate the probability they will violate safety specifications. Two common issues arise when using existing techniques to produce this estimation: If violations occur rarely, simple Monte-Carlo sampling techniques can fail to produce efficient estimates; if simulation horizons are too long, importance sampling techniques (which learn proposal distributions from past simulations) can fail to converge. This paper addresses both issues by interleaving rare-event sampling techniques with online specification monitoring algorithms. We use adaptive multilevel splitting to decompose simulations into partial trajectories, then calculate the distance of those partial trajectories to failure by leveraging robustness metrics from Signal Temporal Logic (STL). By caching those partial robustness metric values, we can efficiently re-use computations across multiple sampling stages. Our experiments on an interstate lane-change scenario show our method is viable for testing simulated AV-pipelines, efficiently estimating failure probabilities for STL specifications based on real traffic rules. We produce better estimates than Monte-Carlo and importance sampling in fewer simulations. Craig Innes, Subramanian Ramamoorthy |
IROS | 2 |
| 2024 | Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion DescriptorsabstractPrecise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics. Current approaches for this task heavily depend on having a significant number of training instances to handle objects with pronounced visual and/or geometric part ambiguities. Our work explores the grounding of fine-grained part descriptors for precise manipulation in a zero-shot setting by utilizing web-trained text-to-image diffusion-based generative models. We tackle the problem by framing it as a dense semantic part correspondence task. Our model returns a gripper pose for manipulating a specific part, using as reference a user-defined click from a source image of a visually different instance of the same object. We require no manual grasping demonstrations as we leverage the intrinsic object geometry and features. Practical experiments in a real-world tabletop scenario validate the efficacy of our approach, demonstrating its potential for advancing semantic-aware robotics manipulation.Web page: https://tsagkas.github.io/click2grasp Nikolaos Tsagkas, Jack Rome, Subramanian Ramamoorthy, Oisin Mac Aodha, Xiaoxuan Lu 0001 |
IROS | 3 |
| 2023 | Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error ModelsabstractTesting black-box perceptual-control systems in simulation faces two difficulties. Firstly, perceptual inputs in simulation lack the fidelity of real-world sensor inputs. Secondly, for a reasonably accurate perception system, encountering a rare failure trajectory may require running infeasibly many simulations. This paper combines perception error models-surrogates for a sensor-based detection system-with state-dependent adaptive importance sampling. This allows us to efficiently assess the rare failure probabilities for real-world perceptual control systems within simulation. Our experiments with an autonomous braking system equipped with an RGB obstacle-detector show that our method can calculate accurate failure probabilities with an inexpensive number of simulations. Further, we show how choice of safety metric can influence the process of learning proposal distributions capable of reliably sampling high-probability failures. Craig Innes, Subramanian Ramamoorthy |
ICRA | 2 |
| 2023 | Learning Robotic Cutting from Demonstration: Non-Holonomic DMPs using the Udwadia-Kalaba MethodabstractDynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manipulation skills from human demonstration. However, the reactive nature of DMPs restricts their applicability for tool use and object manipulation tasks involving non-holonomic constraints, such as scalpel cutting or catheter steering. In this work, we extend the Cartesian space DMP formulation by adding a coupling term that enforces a pre-defined set of non-holonomic constraints. We obtain the closed-form expression for the constraint forcing term using the Udwadia-Kalaba method. This approach offers a clean and practical solution for guaranteed constraint satisfaction at run-time. Further, the proposed analytical form of the constraint forcing term enables efficient trajectory optimization subject to constraints. We demonstrate the usefulness of this approach by showing how we can learn robotic cutting skills from human demonstration. Arturas Straizys, Michael Burke, Subramanian Ramamoorthy |
ICRA | 3 |
| 2023 | Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?abstractAutonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focus on quantitative error metrics like the root mean square error (RMSE) to learn and report their models’ capabilities. This focus on quantitative error metrics tends to ignore the more important behavioral aspect of the models, raising the question of whether these models really predict human-like behavior. Thus, we propose to analyze the output of machine-learned models much like we would analyze human data in conventional behavioral research. We introduce quantitative metrics to demonstrate presence of three different behavioral phenomena in a naturalistic highway driving dataset: 1) The kinematics-dependence of who passes a merging point first 2) Lane change by an on-highway vehicle to accommodate an on-ramp vehicle 3) Lane changes by vehicles on the highway to avoid lead vehicle conflicts. Then, we analyze the behavior of three machine-learned models using the same metrics. Even though the models’ RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior. Additionally, the collision aversion analysis during lane changes showed that the models struggled to capture the physical aspect of human driving: leaving adequate gap between the vehicles. Thus, our analysis highlighted the inadequacy of simple quantitative metrics and the need to take a broader behavioral perspective when analyzing machine-learned models of human driving predictions. Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula |
IEEE Trans. Intell. Transp. Syst. | 8 |
| 2022 | Automated Testing With Temporal Logic Specifications for Robotic Controllers Using Adaptive Experiment DesignabstractMany robot control scenarios involve assessing system robustness against a task specification. If either the controller or environment are composed of “black-box” components with unknown dynamics, we cannot rely on formal verification to assess our system. Assessing robustness via exhaustive testing is also often infeasible if the number of possible environments is large compared to experiment cost. Given limited budget, we provide a method to choose experiment inputs which accurately reflect how robustly a system satisfies a given specification across the domain. By combining signal temporal logic metrics with adaptive experiment design, our method chooses each experiment by incrementally constructing a surrogate model of the specification robustness. This model then chooses experiments in areas of either high prediction error or high uncertainty. Our evaluation shows how this adaptive experiment design results in sample-efficient descriptions of system robustness. Further, we show how to use the constructed surrogate model to assess the behaviour of a data-driven control system under domain shift. Craig Innes, Subramanian Ramamoorthy |
ICRA | 2 |
| 2022 | Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion ProfilesabstractMotion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction, a classical control-based trajectory generator and a mixture of experts collection of light-weight neural networks specialised in motion profile prediction. In contrast to many alternative methods, this modularity helps isolate performance factors and better interpret results, without compromising performance. This system addresses multiple aspects of interest, namely multi-modality, motion profile uncertainty and trajectory physical feasibility. We report on several experiments with the popular highway dataset NGSIM, demonstrating state-of-the-art performance in terms of trajectory error. We also perform a detailed analysis of our system's components, along with experiments that stratify the data based on behaviours, such as change-lane versus follow-lane, to provide insights into the challenges in this domain. Finally, we present a qualitative analysis to show other benefits of our approach, such as the ability to interpret the outputs. Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy |
IROS | 5 |
| 2022 | Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuatorsabstractSoft actuators offer a safe, adaptable approach to tasks like gentle grasping and dexterous manipulation. Creating accurate models to control such systems however is challenging due to the complex physics of deformable materials. Accurate Finite Element Method (FEM) models incur prohibitive computational complexity for closed-loop use. Using a differentiable simulator is an attractive alternative, but their applicability to soft actuators and deformable materials remains under-explored. This paper presents a framework that combines the advantages of both. We learn a differentiable model consisting of a material properties neural network and an analytical dynamics model of the remainder of the manipulation task. This physics-informed model is trained using data generated from FEM, and can be used for closed-loop control and inference. We evaluate our framework on a dielectric elastomer actuator (DEA) coin-pulling task. We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference. Our model attains ≤ 5% simulation error compared to FEM, and we use it as the basis for an MPC controller that requires fewer iterations to converge than model-free actor-critic, PD, and heuristic policies. Manu Lahariya, Craig Innes, Chris Develder, Subramanian Ramamoorthy |
IROS | 4 |
| 2022 | A Novel Design and Evaluation of a Dactylus-Equipped Quadruped Robot for Mobile ManipulationabstractQuadruped robots are usually equipped with ad-ditional arms for manipulation, negatively impacting price and weight. On the other hand, the requirements of legged locomotion mean that the legs of such robots often possess the needed torque and precision to perform manipulation. In this paper, we present a novel design for a small-scale quadruped robot equipped with two leg-mounted manipulators inspired by crustacean chelipeds and knuckle-walker forelimbs. By making use of the actuators already present in the legs, we can achieve manipulation using only 3 additional motors per limb. The design enables the use of small and inexpensive actuators relative to the leg motors, further reducing cost and weight. The moment of inertia impact on the leg is small thanks to an integrated cable/pulley system. As we show in a suite of tele-operation experiments, the robot is capable of performing single- and dual-limb manipulation, as well as transitioning between manipulation modes. The proposed design performs similarly to an additional arm while weighing and costing 5 times less per manipulator and enabling the completion of tasks requiring 2 manipulators. Yordan Tsvetkov, Subramanian Ramamoorthy |
IROS | 2 |
| 2022 | Risk-Driven Design of Perception SystemsabstractModern autonomous systems rely on perception modules to process complex sensor measurements into state estimates. These estimates are then passed to a controller, which uses them to make safety-critical decisions. It is therefore important that we design perception systems to minimize errors that reduce the overall safety of the system. We develop a risk-driven approach to designing perception systems that accounts for the effect of perceptual errors on the performance of the fully-integrated, closed-loop system. We formulate a risk function to quantify the effect of a given perceptual error on overall safety, and show how we can use it to design safer perception systems by including a risk-dependent term in the loss function and generating training data in risk-sensitive regions. We evaluate our techniques on a realistic vision-based aircraft detect and avoid application and show that risk-driven design reduces collision risk by 37% over a baseline system. Anthony Corso 0001, Sydney M. Katz, Craig Innes, Xin Du 0006, Subramanian Ramamoorthy, Mykel J. Kochenderfer |
NeurIPS | 5 |
| 2022 | Formation control for UAVs using a Flux Guided approachabstractExisting studies on formation control for unmanned aerial vehicles (UAV) have not considered encircling targets where an optimum coverage of the target is required at all times. Such coverage plays a critical role in many real-world applications such as tracking hostile UAVs. This paper proposes a new path planning approach called the Flux Guided (FG) method, which generates collision-free trajectories for multiple UAVs while maximising the coverage of target(s). Our method enables UAVs to track directly toward a target whilst maintaining maximum coverage. Furthermore, multiple scattered targets can be tracked by scaling the formation during flight. FG is highly scalable since it only requires communication between sub-set of UAVs on the open boundary of the formation’s surface. Experimental results further validate that FG generates UAV trajectories 1.5× shorter than previous work and that trajectory planning for 9 leader/follower UAVs to surround a target in two different scenarios only requires 0.52 s and 0.88 s, respectively. The resulting trajectories are suitable for robotic controls after time-optimal parameterisation; we demonstrate this using a 3d dynamic particle system that tracks the desired trajectories using a PID controller. John Hartley, Hubert P. H. Shum, Edmond S. L. Ho, He Wang 0002, Subramanian Ramamoorthy |
Expert Syst. Appl. | 5 |
| 2022 | A Two-Stage Optimization-Based Motion Planner for Safe Urban DrivingabstractRecent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, nonconvex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: In the first stage, we find a solution to a mixed-integer linear programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second nonlinear programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort, and progress. Francisco Girbal Eiras, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy |
IEEE Trans. Robotics | 4 |
| 2021 | Learning from Demonstration with Weakly Supervised Disentanglement
Yordan Hristov, Subramanian Ramamoorthy |
ICLR | 2 |
| 2021 | Interpretable Goal-based Prediction and Planning for Autonomous DrivingabstractWe propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS utilise a shared set of defined maneuvers and macro actions to construct plans which are explainable by means of rationality principles. Evaluation in simulations of urban driving scenarios demonstrate the system’s ability to robustly recognise the goals of other vehicles, enabling our vehicle to exploit non-trivial opportunities to significantly reduce driving times. In each scenario, we extract intuitive explanations for the predictions which justify the system’s decisions. Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Girbal Eiras, Mihai Dobre, Subramanian Ramamoorthy |
ICRA | 7 |
| 2021 | ProbRobScene: A Probabilistic Specification Language for 3D Robotic Manipulation EnvironmentsabstractRobotic control tasks are often first run in simulation for the purposes of verification, debugging and data augmentation. Many methods exist to specify what task a robot must complete, but few exist to specify what range of environments a user expects such tasks to be achieved in. ProbRobScene is a probabilistic specification language for describing robotic manipulation environments. Using the language, a user need only specify the relational constraints that must hold between objects in a scene. ProbRobScene then automatically generates scenes which conform to this specification. By combining aspects of probabilistic programming languages and convex geometry, we provide a method for sampling this space of possible environments efficiently. We demonstrate the usefulness of our language by using it to debug a robotic controller in a tabletop robot manipulation environment. Craig Innes, Subramanian Ramamoorthy |
ICRA | 2 |
| 2021 | Building Affordance Relations for Robotic Agents - A ReviewabstractAffordances describe the possibilities for an agent to perform actions with an object. While the significance of the affordance concept has been previously studied from varied perspectives, such as psychology and cognitive science, these approaches are not always sufficient to enable direct transfer, in the sense of implementations, to artificial intelligence (AI)-based systems and robotics. However, many efforts have been made to pragmatically employ the concept of affordances, as it represents great potential for AI agents to effectively bridge perception to action. In this survey, we review and find common ground amongst different strategies that use the concept of affordances within robotic tasks, and build on these methods to provide guidance for including affordances as a mechanism to improve autonomy. To this end, we outline common design choices for building representations of affordance relations, and their implications on the generalisation capabilities of an agent when facing previously unseen scenarios. Finally, we identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent. Paola Ardón Ramirez, Èric Pairet, Katrin S. Lohan, Subramanian Ramamoorthy, Ronald P. A. Petrick |
IJCAI | 4 |
| 2021 | Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous VehiclesabstractRecognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When there are unseen obstacles or occluded vehicles in a scenario, goal recognition may be confounded by the effects of these unseen entities on the behaviour of observed vehicles. Existing prediction algorithms that assume rational behaviour with respect to inferred goals may fail to make accurate long-horizon predictions because they ignore the possibility that the behaviour is influenced by such unseen entities. We introduce the Goal and Occluded Factor Inference (GOFI) algorithm which bases inference on inverse-planning to jointly infer a probabilistic belief over goals and potential occluded factors. We then show how these beliefs can be integrated into Monte Carlo Tree Search (MCTS). We demonstrate that jointly inferring goals and occluded factors leads to more accurate beliefs with respect to the true world state and allows an agent to safely navigate several scenarios where other baselines take unsafe actions leading to collisions. Josiah Hanna, Arrasy Rahman, Elliot Fosong, Francisco Girbal Eiras, Mihai Dobre, John Redford, Subramanian Ramamoorthy, Stefano V. Albrecht |
IROS | 7 |
| 2021 | PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous DrivingabstractAchieving a proper balance between planning quality, safety and efficiency is a major challenge for autonomous driving. Optimisation-based motion planners are capable of producing safe, smooth and comfortable plans, but often at the cost of runtime efficiency. On the other hand, naïvely deploying trajectories produced by efficient-to-run deep imitation learning approaches might risk compromising safety. In this paper, we present PILOT a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network’s plan, guaranteeing fulfilment of safety and comfort requirements. The objective of the efficient optimiser is the same as the objective of an expensive-to-run optimisation-based planning system that the neural network is trained offline to imitate. This efficient optimiser provides a key layer of online protection from learning failures or deficiency in out-of-distribution situations that might compromise safety or comfort. Using a state-of-the-art, runtime-intensive optimisation-based method as the expert, we demonstrate in simulated autonomous driving experiments in CARLA that PILOT achieves a seven-fold reduction in runtime when compared to the expert it imitates without sacrificing planning quality. Henry Pulver, Francisco Girbal Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy |
IROS | 6 |
| 2021 | Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous RobotsabstractUnderstanding a controller’s performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynamics of the world, which is often the case in complex domains, we may need to approximate a performance function of the robot based on its interaction with the environment. Such a performance function gives us insights into the behaviour of the robot, allowing us to fine-tune the controller with manual interventions. In high-dimensionality systems, where the action-state space is large, fine-tuning a controller is non-trivial. To overcome this problem, we propose a performance function whose domain is defined by external features and parameters of the controller. Attainment regions are defined over such a domain defined by feature-parameter pairs, and serve the purpose of enabling prediction of successful execution of the task. The use of the feature-parameter space –in contrast to the action-state space– allows us to adapt, explain and fine-tune the controller over a simpler (i.e., lower dimensional) space. When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness. When the robot fails to execute the task, we use the regions to debug the controller and find adaptive and counterfactual changes to the solutions. Another advantage of this approach is that we can generalise through the use of Gaussian processes regression of the performance function in the high-dimensional space. To test our approach, we demonstrate learning an approximation to the performance function in simulation, with a mobile robot traversing different terrain conditions. Then, with a sample-efficient method, we propagate the attainment regions to a physical robot in a similar environment. Simón C. Smith, Subramanian Ramamoorthy |
IROS | 2 |
| 2020 | Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classificationabstractPrecision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we consider the challenge of cutting along the boundary between two soft mediums, a problem that is made extremely difficult due to visibility constraints, which means that the precise location of the cutting trajectory is typically unknown. This paper introduces a novel strategy to address this task, using a binary medium classifier trained using joint torque measurements, and a closed loop control law that relies on an error signal compactly encoded in the decision boundary of the classifier. We illustrate this on a grapefruit cutting task, successfully modulating a nominal trajectory t using dynamic movement primitives to follow the boundary between grapefruit pulp and peel using torque based medium classification. Results show that this control strategy is successful in 72 % of attempts in contrast to control using a nominal trajectory, which only succeeds in 50 % of attempts. Arturas Straizys, Michael Burke, Subramanian Ramamoorthy |
ICRA | 3 |
| 2020 | Self-Assessment of Grasp Affordance TransferabstractReasoning about object grasp affordances allows an autonomous agent to estimate the most suitable grasp to execute a task. While current approaches for estimating grasp affordances are effective, their prediction is driven by hypotheses on visual features rather than an indicator of a proposal's suitability for an affordance task. Consequently, these works cannot guarantee any level of performance when executing a task and, in fact, not even ensure successful task completion. In this work, we present a pipeline for self-assessment of grasp affordance transfer (SAGAT) based on prior experiences. We visually detect a grasp affordance region to extract multiple grasp affordance configuration candidates. Using these candidates, we forward simulate the outcome of executing the affordance task to analyse the relation between task outcome and grasp candidates. The relations are ranked by performance success with a heuristic confidence function and used to build a library of affordance task experiences. The library is later queried to perform one-shot transfer estimation of the best grasp configuration on new objects. Experimental evaluation shows that our method exhibits a significant performance improvement up to 11.7% against current state-of-the-art methods on grasp affordance detection. Experiments on a PR2 robotic platform demonstrate our method's highly reliable deployability to deal with real-world task affordance problems. Paola Ardón Ramirez, Èric Pairet, Yvan R. Petillot, Ronald P. A. Petrick, Subramanian Ramamoorthy, Katrin S. Lohan |
IROS | 5 |
| 2020 | Stir to Pour: Efficient Calibration of Liquid Properties for Pouring ActionsabstractHumans use simple probing actions to develop intuition about the physical behavior of common objects. Such intuition is particularly useful for adaptive estimation of favorable manipulation strategies of those objects in novel contexts. For example, observing the effect of tilt on a transparent bottle containing an unknown liquid provides clues on how the liquid might be poured. It is desirable to equip general-purpose robotic systems with this capability because it is inevitable that they will encounter novel objects and scenarios. In this paper, we teach a robot to use a simple, specified probing strategy - stirring with a stick- to reduce spillage when pouring unknown liquids. In the probing step, we continuously observe the effects of a real robot stirring a liquid, while simultaneously tuning the parameters to a model (simulator) until the two outputs are in agreement. We obtain optimal simulation parameters, characterizing the unknown liquid, via a Bayesian Optimizer that minimizes the discrepancy between real and simulated outcomes. Then, we optimize the pouring policy conditioning on the optimal simulation parameters determined via stirring. We show that using stirring as a probing strategy result in reduced spillage for three qualitatively different liquids when executed on a UR10 Robot, compared to probing via pouring. Finally, we provide quantitative insights into the reason for stirring being a suitable calibration task for pouring -a step towards automatic discovery of probing strategies. Tatiana Lopez-Guevara, Rita Pucci, Nick K. Taylor, Michael U. Gutmann, Subramanian Ramamoorthy, Kartic Subr |
IROS | 5 |
| 2020 | From demonstrations to task-space specifications. Using causal analysis to extract rule parameterization from demonstrationsabstractAbstract Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human–robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable models. An advantage of structuring generative models in this way is that we can extract causal relationships between symbols that might form part of the user’s specification of the task, as manifested in the demonstrations. We further parameterize these specifications through constraint optimization in order to find a safety envelope under which motion planning can be performed. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8–99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task. Daniel Angelov, Yordan Hristov, Subramanian Ramamoorthy |
Auton. Agents Multi Agent Syst. | 3 |
| 2020 | Lower dimensional kernels for video discriminators
Emmanuel Kahembwe, Subramanian Ramamoorthy |
Neural Networks | 2 |
| 2019 | Learning Programmatically Structured Representations with Perceptor Gradients
Svetlin Penkov, Subramanian Ramamoorthy |
ICLR (Poster) | 2 |
| 2018 | Learning Best Response Strategies for Agents in Ad Exchanges
Stavros Gerakaris, Subramanian Ramamoorthy |
EUMAS | 2 |
| 2018 | Topological signatures for fast mobility analysisabstractAnalytic methods can be difficult to build and costly to train for mobility data. We show that information about the topology of the space and how mobile objects navigate the obstacles can be used to extract insights about mobility at larger distance scales. The main contribution of this paper is a topological signature that maps each trajectory to a relatively low dimensional Euclidean space, so that now they are amenable to standard analytic techniques. Data mining tasks: nearest neighbor search with locality sensitive hashing, clustering, regression, etc., work more efficiently in this signature space. We define the problem of mobility prediction at different distance scales, and show that with the signatures simple k nearest neighbor based regression perform accurate prediction. Experiments on multiple real datasets show that the framework using topological signatures is accurate on all tasks, and substantially more efficient than machine learning applied to raw data. Theoretical results show that the signatures contain enough topological information to reconstruct non-self-intersecting trajectories upto homotopy type. The construction of signatures is based on a differential form that can be generated in a distributed setting using local communication, and a signature can be locally and inexpensively updated and communicated by a mobile agent. Abhirup Ghosh, Benedek Rozemberczki, Subramanian Ramamoorthy, Rik Sarkar |
SIGSPATIAL/GIS | 3 |
| 2017 | Physical symbol grounding and instance learning through demonstration and eye trackingabstractIt is natural for humans to work with abstract plans which are often an intuitive and concise way to represent a task. However, high level task descriptions contain symbols and concepts which need to be grounded within the environment if the plan is to be executed by an autonomous robot. The problem of learning the mapping between abstract plan symbols and their physical instances in the environment is known as the problem of physical symbol grounding. In this paper, we propose a framework for Grounding and Learning Instances through Demonstration and Eye tracking (GLIDE). We associate traces of task demonstration to a sequence of fixations which we call fixation programs and exploit their properties in order to perform physical symbol grounding. We formulate the problem as a probabilistic generative model and present an algorithm for computationally feasible inference over the proposed model. A key aspect of our work is that we estimate fixation locations within the environment which enables the appearance of symbol instances to be learnt. Instance learning is a crucial ability when the robot does not have any knowledge about the model or the appearance of the symbols referred to in the plan instructions. We have conducted human experiments and demonstrate that GLIDE successfully grounds plan symbols and learns the appearance of their instances, thus enabling robots to autonomously execute tasks in initially unknown environments. Svetlin Penkov, Alejandro Bordallo, Subramanian Ramamoorthy |
ICRA | 3 |
| 2017 | Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)abstractDynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity. Stefano V. Albrecht, Subramanian Ramamoorthy |
IJCAI | 2 |
| 2016 | Predicting Future Agent Motions for Dynamic EnvironmentsabstractUnderstanding activities of people in a monitored environment is a topic of active research, motivated by applications requiring context-awareness. Inferring future agent motion is useful not only for improving tracking accuracy, but also for planning in an interactive motion task. Despite rapid advances in the area of activity forecasting, many state-of-the-art methods are still cumbersome for use in realistic robots. This is due to the requirement of having good semantic scene and map labelling, as well as assumptions made regarding possible goals and types of motion. Many emerging applications require robots with modest sensory and computational ability to robustly perform such activity forecasting in high density and dynamic environments. We address this by combining a novel multi-camera tracking method, efficient multi-resolution representations of state and a standard Inverse Reinforcement Learning (IRL) technique, to demonstrate performance that is better than the state-of-the-art in the literature. In this framework, the IRL method uses agent trajectories from a distributed tracker and estimates a reward function within a Markov Decision Process (MDP) model. This reward function can then be used to estimate the agent's motion in future novel task instances. We present empirical experiments using data gathered in our own lab and external corpora (VIRAT), based on which we find that our algorithm is not only efficiently implementable on a resource constrained platform but is also competitive in terms of accuracy with state-of-the-art alternatives (e.g., up to 20% better than the results reported in [1]). Fabio Previtali, Alejandro Bordallo, Luca Iocchi, Subramanian Ramamoorthy |
ICMLA | 4 |
| 2016 | Automatic configuration of ROS applications for near-optimal performanceabstractThe performance of a ROS application is a function of the individual performance of its constituent nodes. Since ROS nodes are typically configurable (parameterised), the specific parameter values adopted will determine the level of performance generated. In addition, ROS applications may be distributed across multiple computation devices, thus providing different options for node allocation. We address two configuration problems that the typical ROS user is confronted with: i) Determining parameter values and node allocations for maximising performance; ii) Determining node allocations for minimising hardware resources that can guarantee the desired performance. We formalise these problems with a mathematical model, a constrained form of a multiple-choice multiple knapsack problem. We propose a greedy algorithm for optimising each problem, using linear regression for predicting the performance of an individual ROS node over a continuum set of parameter combinations. We evaluate the algorithms through simulation and we validate them in a real ROS scenario, showing that the expected performance levels only deviate from the real measurements by an average of 2.5%. José Cano 0001, Alejandro Bordallo, Vijay Nagarajan, Subramanian Ramamoorthy, Sethu Vijayakumar |
IROS | 4 |
| 2016 | Belief and truth in hypothesised behaviours
Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy |
Artif. Intell. | 3 |
| 2016 | Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian NetworksabstractDynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task. Stefano V. Albrecht, Subramanian Ramamoorthy |
J. Artif. Intell. Res. | 2 |
| 2016 | Bayesian policy reuse
Benjamin Rosman, Majd Hawasly, Subramanian Ramamoorthy |
Mach. Learn. | 3 |
| 2015 | An Empirical Study on the Practical Impact of Prior Beliefs over Policy TypesabstractMany multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically. Stefano V. Albrecht, Jacob W. Crandall, Subramanian Ramamoorthy |
AAAI | 3 |
| 2015 | Counterfactual reasoning about intent for interactive navigation in dynamic environmentsabstractMany modern robotics applications require robots to function autonomously in dynamic environments including other decision making agents, such as people or other robots. This calls for fast and scalable interactive motion planning. This requires models that take into consideration the other agent's intended actions in one's own planning. We present a real-time motion planning framework that brings together a few key components including intention inference by reasoning counterfactually about potential motion of the other agents as they work towards different goals. By using a light-weight motion model, we achieve efficient iterative planning for fluid motion when avoiding pedestrians, in parallel with goal inference for longer range movement prediction. This inference framework is coupled with a novel distributed visual tracking method that provides reliable and robust models for the current belief-state of the monitored environment. This combined approach represents a computationally efficient alternative to previously studied policy learning methods that often require significant offline training or calibration and do not yet scale to densely populated environments. We validate this framework with experiments involving multi-robot and human-robot navigation. We further validate the tracker component separately on much larger scale unconstrained pedestrian data sets. Alejandro Bordallo, Fabio Previtali, Nantas Nardelli, Subramanian Ramamoorthy |
IROS | 4 |
| 2015 | Are You Doing What I Think You Are Doing? Criticising Uncertain Agent Models
Stefano V. Albrecht, Subramanian Ramamoorthy |
UAI | 2 |
| 2014 | A Generative Model for User Simulation in a Spatial Navigation DomainabstractWe propose the use of a generative model to simulate user behaviour in a novel taskoriented dialog domain, where user goals are spatial routes across artificial landscapes.We show how to derive an efficient feature-based representation of spatial goals, admitting exact inference and generalising to new routes.The use of a generative model allows us to capture a range of plausible behaviour given the same underlying goal.We evaluate intrinsically using held-out probability and perplexity, and find a substantial reduction in uncertainty brought by our spatial representation.We evaluate extrinsically in a human judgement task and find that our model's behaviour does not differ significantly from the behaviour of real users. Aciel Eshky, Ben Allison, Subramanian Ramamoorthy, Mark Steedman |
EACL | 3 |
| 2014 | Giving advice to agents with hidden goalsabstractThis paper considers the problem of providing advice to an autonomous agent when neither the behavioural policy nor the goals of that agent are known to the advisor. We present an approach based on building a model of “common sense” behaviour in the domain, from an aggregation of different users performing various tasks, modelled as MDPs, in the same domain. From this model, we estimate the normalcy of the trajectory given by a new agent in the domain, and provide behavioural advice based on an approximation of the trade-off in utility between potential benefits to the exploring agent and the costs incurred in giving this advice. This model is evaluated on a maze world domain by providing advice to different types of agents, and we show that this leads to a considerable and unanimous improvement in the completion rate of their tasks. Benjamin Rosman, Subramanian Ramamoorthy |
ICRA | 2 |
| 2014 | Joint classification of actions and object state changes with a latent variable discriminative modelabstractWe present a technique to classify human actions that involve object manipulation. Our focus is to accurately distinguish between actions that are related in that the object's state changes define the essential differences. Our algorithm uses a latent variable conditional random field that allows for the modelling of spatio-temporal relationships between the human motion and the corresponding object state changes. Our approach involves a factored representation that better allows for the description of causal effects in the way human action causes object state changes. The utility of incorporating such structure in our model is that it enables more accurate classification of activities that could enable robots to reason about interaction, and to learn using a high level vocabulary that captures phenomena of interest. We present experiments involving the recognition of human actions, where we show that our factored representation achieves superior performance in comparison to alternate flat representations. Efstathios Vafeias, Subramanian Ramamoorthy |
ICRA | 2 |
| 2014 | On user behaviour adaptation under interface changeabstractDifferent interfaces allow a user to achieve the same end goal through different action sequences, e.g., command lines vs. drop down menus. Interface efficiency can be described in terms of a cost incurred, e.g., time taken, by the user in typical tasks. Realistic users arrive at evaluations of efficiency, hence making choices about which interface to use, over time, based on trial and error experience. Their choices are also determined by prior experience, which determines how much learning time is required. These factors have substantial effect on the adoption of new interfaces. In this paper, we aim at understanding how users adapt under interface change, how much time it takes them to learn to interact optimally with an interface, and how this learning could be expedited through intermediate interfaces. We present results from a series of experiments that make four main points: (a) different interfaces for accomplishing the same task can elicit significant variability in performance, (b) switching interfaces can result in adverse sharp shifts in performance, (c) subject to some variability, there are individual thresholds on tolerance to this kind of performance degradation with an interface, causing users to potentially abandon what may be a pretty good interface, and (d) our main result -- shaping user learning through the presentation of intermediate interfaces can mitigate the adverse shifts in performance while still enabling the eventual improved performance with the complex interface upon the user becoming suitably accustomed. In our experiments, human users use keyboard based interfaces to navigate a simulated ball through a maze. Our results are a first step towards interface adaptation algorithms that architect choice to accommodate personality traits of realistic users. Benjamin Rosman, Subramanian Ramamoorthy, M. M. Hassan Mahmud, Pushmeet Kohli |
IUI | 2 |
| 2014 | A Formalization of the Coach Problem
Gwendolijn Y. R. Schropp, John-Jules Ch. Meyer, Subramanian Ramamoorthy |
RoboCup | 3 |
| 2014 | On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems
Stefano V. Albrecht, Subramanian Ramamoorthy |
UAI | 2 |
| 2013 | Evaluating the effects of limited perception on interactive decisions in mixed robotic domains
Aris Valtazanos, Subramanian Ramamoorthy |
HRI | 2 |
| 2013 | Using wearable inertial sensors for posture and position tracking in unconstrained environments through learned translation manifoldsabstractDespite recent advances in 3-D motion capture, the problem of simultaneously tracking human posture and position in an unconstrained environment remains open. Optical systems provide both types of information, but are confined to a restricted area of capture. Inertial sensing alleviates this restriction, but at the expense of capturing only relative (postural) and not absolute (positional) information. In this paper, we propose an algorithm combining the relative merits of these systems to track both position and posture in challenging environments. Offline, we combine an optical (Kinect) and an inertial sensing (Orient-4) platform to learn a mapping from posture variations to translations, which we encode as a translation manifold. Online, the optical source is removed, and the learned mapping is used to infer positions using the postures computed by the inertial sensors. We first evaluate our approach in simulation, on motion sequences with ground-truth positions for error estimation. Then, the method is deployed on physical sensing platforms to track human subjects. The proposed algorithm is shown to yield a lower average cumulative error than comparable position tracking methods, such as double integration of accelerometer data, on both simulated and real sensory data, and in a variety of motions and capture settings. Aris Valtazanos, D. K. Arvind 0001, Subramanian Ramamoorthy |
IPSN | 3 |
| 2013 | Lifelong transfer learning with an option hierarchyabstractMany applications require autonomous agents to achieve quick responses to task instances drawn from a rich family of qualitatively-related tasks. We address the setting where the tasks share a state-action space and have the same qualitative objective but differ in dynamics. We adopt a transfer learning approach where common structure in previously-learnt policies, in the form of shared subtasks, is exploited to accelerate learning in subsequent ones. We use a probabilistic mixture model to describe regions in state space which are common to successful trajectories in different instances. Then, we extract policy fragments from previously-learnt policies that are specialised to these regions. These policy fragments are options, whose initiation and termination sets are automatically extracted from data by the mixture model. In novel task instances, these options are used in an SMDP learning process and option learning repeats over the resulting policy library. The utility of this method is demonstrated through experiments in a standard navigation environment and then in the RoboCup simulated soccer domain with opponent teams of different skill. Majd Hawasly, Subramanian Ramamoorthy |
IROS | 2 |
| 2013 | BRISK-Based Visual Feature Extraction for Resource Constrained Robots
Daniel J. Mankowitz, Subramanian Ramamoorthy |
RoboCup | 2 |
| 2013 | Latent space segmentation for mobile gait analysisabstractAn unsupervised learning algorithm is presented for segmentation and evaluation of motion data from the on-body Orient wireless motion capture system for mobile gait analysis. The algorithm is model-free and operates on the latent space of the motion, by first aggregating all the sensor data into a single vector, and then modeling them on a low-dimensional manifold to perform segmentation. The proposed approach is contrasted to a basic, model-based algorithm, which operates directly on the joint angles computed by the Orient sensor devices. The latent space algorithm is shown to be capable of retrieving qualitative features of the motion even in the face of noisy or incomplete sensor readings. Aris Valtazanos, D. K. Arvind 0001, Subramanian Ramamoorthy |
ACM Trans. Embed. Comput. Syst. | 3 |
| 2011 | Intent inference and strategic escape in multi-robot games with physical limitations and uncertaintyabstractMany multi-robot decision problems present autonomous agents with a dual challenge: the accurate egocentric estimation of the state and strategy of their adversaries, in the face of physical limitations and sensory uncertainty. Although these are clearly difficult constraints on the capabilities of an autonomous robot, this is also an opportunity for exploiting the corresponding limitations of the adversary. In this paper, we propose a decision making framework for physically constrained multi-robot games, using a combination of probabilistic and game-theoretic tools. We first present the Reachable Set Particle Filter, an adversary state estimation algorithm combining data-driven approximation with dynamical constraints. Then, we use game-theoretic notions to formulate a strategy estimation framework that progressively learns and exploits the adversary's behaviour. We evaluate our framework in a series of robotic soccer games between robots with varying sensing and strategic capabilities. Our results demonstrate that the combination of probabilistic modeling and strategic reasoning leads to significant improvements in performance robustness, while flexibly adapting to dynamic adversaries. Aris Valtazanos, Subramanian Ramamoorthy |
IROS | 2 |
| 2011 | Reinforcement Learning through Global Stochastic Search in N-MDPs
Matteo Leonetti, Luca Iocchi, Subramanian Ramamoorthy |
ECML/PKDD (2) | 3 |
| 2011 | Online Motion Planning for Multi-robot Interaction Using Composable Reachable Sets
Aris Valtazanos, Subramanian Ramamoorthy |
RoboCup | 2 |
| 2011 | NaOISIS: A 3-D Behavioural Simulator for the NAO Humanoid Robot
Aris Valtazanos, Subramanian Ramamoorthy |
RoboCup | 2 |
| 2010 | Geodesic trajectory generation on learnt skill manifoldsabstractHumanoid robots are appealing due to their inherent dexterity. However, these potential benefits may only be realized if the corresponding motion synthesis procedure is suitably flexible. This paper presents a flexible trajectory generation algorithm that utilizes a geometric representation of humanoid skills (e.g., walking) - in the form of skill manifolds. These manifolds are learnt from demonstration data that may be obtained from off-line optimization algorithms (or a human expert).We demonstrate that this model may be used to produce approximately optimal motion plans as geodesics over the manifold and that this allows us to effectively generalize from a limited training set. We demonstrate the effectiveness of our approach on a simulated 3-link planar arm, and then the more challenging example of a physical 19-DoF humanoid robot. We show that our algorithm produces a close approximation of the much more computationally intensive optimization procedure used to generate the data. This allows us to present experimental results for fast motion planning on a realistic - variable step length, width and height - walking task on a humanoid robot. Ioannis Havoutis, Subramanian Ramamoorthy |
ICRA | 2 |
| 2010 | An efficient algorithm for self-reconfiguration planning in a modular robotabstractAn efficient planning algorithm for the hexagonal metamorphic self-reconfiguring system (SRS) is presented. Empirically, the algorithm achieves an time complexity of O(n) averaged over random problem instances. The planning algorithm is capable of solving approximately 97% of planning tasks in the general state space of configurations containing less than 20,000 units. The state space is divided into two classes according to planning efficiency. The configurations belonging to the first class permit an Euler tour to be wrapped around the robotic aggregate. The existence of the Euler tour implies units are free to move around the perimeter of the SRS. Planning between configurations in this class can be performed in O(n) using a specialized planning algorithm. The set of Euler tour configurations span a large volume of the general state space of the hexagonal SRS. A second specialized planning algorithm plans from a general configuration to a nearby Euler tour configuration. While planning in the general configuration state space is computationally harder, the distance required to plan is short. Thus, the combination of both algorithms allows us to efficiently plan for a large proportion of possible reconfiguration tasks for the hexagonal metamorphic robot. Tom Larkworthy, Subramanian Ramamoorthy |
ICRA | 2 |
| 2010 | A game-theoretic procedure for learning hierarchically structured strategiesabstractThis paper addresses the problem of acquiring a hierarchically structured robotic skill in a nonstationary environment. This is achieved through a combination of learning primitive strategies from observation of an expert, and autonomously synthesising composite strategies from that basis. Both aspects of this problem are approached from a game theoretic viewpoint, building on prior work in the area of multiplicative weights learning algorithms. The utility of this procedure is demonstrated through simulation experiments motivated by the problem of autonomous driving. We show that this procedure allows the agent to come to terms with two forms of uncertainty in the world - continually varying goals (due to oncoming traffic) and nonstationarity of optimisation criteria (e.g., driven by changing navigability of the road). We argue that this type of factored task specification and learning is a necessary ingredient for robust autonomous behaviour in a “large-world” setting. Benjamin Rosman, Subramanian Ramamoorthy |
ICRA | 2 |
| 2010 | Constrained geodesic trajectory generation on learnt skill manifoldsabstractThis paper addresses the problem of compactly encoding a continuous family of trajectories corresponding to a robotic skill, and using this representation for the purpose of constrained trajectory generation in an environment with many (possibly dynamic) obstacles. With a skill manifold that is learnt from data, we show that constraints can be naturally handled within an iterative process of minimizing the total geodesic path length and curvature over the manifold. We demonstrate the utility of this process with two examples. Firstly, a three-link arm whose joint space and corresponding skill manifold can be explicitly visualized. Then, we demonstrate how this procedure can be used to generate constrained walking motions in a humanoid robot. Ioannis Havoutis, Subramanian Ramamoorthy |
IROS | 2 |
| 2010 | Controlling humanoid robots in topology coordinatesabstractThis paper presents an approach to the control of humanoid robot motion, e.g., holding another robot or tangled interactions involving multiple limbs, in a space defined by `topology coordinates'. The constraints of tangling can be linearized at every frame of motion synthesis, and can be used together with constraints such as defined by the Zero Moment Point, Center of Mass, inverse kinematics and angular momentum for computing the postures by a linear programming procedure. We demonstrate the utility of this approach using the simulator for the Nao humanoid robot. We show that this approach enables us to synthesize complex motion, such as tangling, very efficiently. Edmond S. L. Ho, Taku Komura, Subramanian Ramamoorthy, Sethu Vijayakumar |
IROS | 3 |
| 2009 | A differential geometric approach to discrete-coefficient filter designabstractThis paper is concerned with the problem of computing a discrete-coefficient approximation to a digital filter. In contrast to earlier works that have approached this problem using standard combinatorial optimization tools, we take a geometric approach. We define a Riemannian manifold, arising from the difference in frequency response between the two systems of interest, on which we design efficient algorithms for sampling and approximation. This additional structure enables us to tame the computational complexity of the native combinatorial optimization problem. We illustrate the benefits of this approach with design examples involving IIR and FIR filters. Subramanian Ramamoorthy, Lothar Wenzel, James Nagle, Michael Cerna |
ICASSP | 1 |
| 2009 | Motion Synthesis through Randomized Exploration on Submanifolds of Configuration Space
Ioannis Havoutis, Subramanian Ramamoorthy |
RoboCup | 2 |
| 2008 | Trajectory generation for dynamic bipedal walking through qualitative model based manifold learningabstractLegged robots represent great promise for transport in unstructured environments. However, it has been difficult to devise motion planning strategies that achieve a combination of energy efficiency, safety and flexibility comparable to legged animals. In this paper, we address this issue by presenting a trajectory generation strategy for dynamic bipedal walking robots using a factored approach to motion planning - combining a low-dimensional plan (based on intermittently actuated passive walking in a compass-gait biped) with a manifold learning algorithm that solves the problem of embedding this plan in the high-dimensional phase space of the robot. This allows us to achieve task level control (over step length) in an energy efficient way - starting with only a coarse qualitative model of the system dynamics and performing a data-driven approximation of the dynamics in order to synthesize families of dynamically realizable trajectories. We demonstrate the utility of this approach with simulation results for a multi-link legged robot. Subramanian Ramamoorthy, Benjamin Kuipers |
ICRA | 1 |
| 2006 | Designing safe, profitable automated stock trading agents using evolutionary algorithmsabstractTrading rules are widely used by practitioners as an effective means to mechanize aspects of their reasoning about stock price trends. However, due to the simplicity of these rules, each rule is susceptible to poor behavior in specific types of adverse market conditions. Naive combinations of such rules are not very effective in mitigating the weaknesses of component rules. We demonstrate that sophisticated approaches to combining these trading rules enable us to overcome these problems and gainfully utilize them in autonomous agents. We achieve this combination through the use of genetic algorithms and genetic programs. Further, we show that it is possible to use qualitative characterizations of stochastic dynamics to improve the performance of these agents by delineating safe, or feasible, regions. We present the results of experiments conducted within the Penn-Lehman Automated Trading project. In this way we are able to demonstrate that autonomous agents can achieve consistent profitability in a variety of market conditions, in ways that are human competitive. Categories and Subject Descriptors [Real World Applications]: finance, intelligent agents, automated Harish Subramanian, Subramanian Ramamoorthy, Peter Stone 0001, Benjamin Kuipers |
GECCO | 2 |
| 2006 | Low-Discrepancy Curves and Efficient Coverage of Space
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